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Introduction to Machine Learning (Fall 2020)
By Massachusetts Institute of Technology, MIT
Length: 13 weeks
π Course link
#ml #machinelearning #datascience #MIT
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By Massachusetts Institute of Technology, MIT
Length: 13 weeks
π Course link
#ml #machinelearning #datascience #MIT
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Introduction to Data Science by University of Washington
π¬ 95 video sessions
β° Duration: 16h
π¨βπ« Instructor: Bill Howe, PhD
β Completely free
π Course link
#datascience #ds #ml #washingtonuniversity
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π¬ 95 video sessions
β° Duration: 16h
π¨βπ« Instructor: Bill Howe, PhD
β Completely free
π Course link
#datascience #ds #ml #washingtonuniversity
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Data Science: Python for Data Analysis 2022 Full Bootcamp
Rating βοΈ: 4.3 out of 5
Students π¨βπ«: 104,287
Created by: Ahmed Ibrahim and SDE OCTOPUS | AI
π Course link
Note: Free coupon is inserted in URL. Number of free spots is limited to 1000. Once this number is reached, coupon won't be valid anymore.
#python #datanalysis #datascience
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Rating βοΈ: 4.3 out of 5
Students π¨βπ«: 104,287
Created by: Ahmed Ibrahim and SDE OCTOPUS | AI
π Course link
Note: Free coupon is inserted in URL. Number of free spots is limited to 1000. Once this number is reached, coupon won't be valid anymore.
#python #datanalysis #datascience
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Udemy
Data Science: Python for Data Analysis Full Bootcamp
Learn and build your Python Programming skills from the ground up in addition to Python Data Science libraries and tools
Free 10-Hour Machine Learning Course
by freecodecamp
Section 1: Basics of Machine Learning
Section 2: Linear Regression & Regularization
Section 3: Logistic Regression & Performance Metrics
Section 4: Support Vector Machine
Section 5: PCA
Section 6: Learning Theory
Section 7: Decision Trees & Random Forest
Section 7.5: Learning more algorithms and building more projects
Section 8: Unsupervised Learning Algorithms
Section 9: Building Applications
π Course link: https://www.freecodecamp.org/news/free-machine-learning-course-10-hourse/
10-hour youtube video: https://www.youtube.com/watch?v=NWONeJKn6kc
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by freecodecamp
Section 1: Basics of Machine Learning
Section 2: Linear Regression & Regularization
Section 3: Logistic Regression & Performance Metrics
Section 4: Support Vector Machine
Section 5: PCA
Section 6: Learning Theory
Section 7: Decision Trees & Random Forest
Section 7.5: Learning more algorithms and building more projects
Section 8: Unsupervised Learning Algorithms
Section 9: Building Applications
π Course link: https://www.freecodecamp.org/news/free-machine-learning-course-10-hourse/
10-hour youtube video: https://www.youtube.com/watch?v=NWONeJKn6kc
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freeCodeCamp.org
Free 10-Hour Machine Learning Course
Every day more and more use cases are found for machine learning. It is a great field to get into. We just released a 10-hour machine learning course for beginners on the freeCodeCamp.org YouTube channel. Ayush Singh developed this course. He is a yo...
ML and NLP Research Highlights of 2021
by Sebastian Ruder
This post summarizes progress across multiple impactful areas in ML and NLP in 2021.
Contents:
Universal Models
Massive Multi-task Learning
Beyond the Transformer
Prompting
Efficient Methods
Benchmarking
Conditional Image Generation
ML for Science
Program Synthesis
Bias
Retrieval Augmentation
Token-free Models
Temporal Adaptation
The Importance of Data
Meta-learning
https://ruder.io/ml-highlights-2021/
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by Sebastian Ruder
This post summarizes progress across multiple impactful areas in ML and NLP in 2021.
Contents:
Universal Models
Massive Multi-task Learning
Beyond the Transformer
Prompting
Efficient Methods
Benchmarking
Conditional Image Generation
ML for Science
Program Synthesis
Bias
Retrieval Augmentation
Token-free Models
Temporal Adaptation
The Importance of Data
Meta-learning
https://ruder.io/ml-highlights-2021/
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ruder.io
ML and NLP Research Highlights of 2021
This post summarizes progress across multiple impactful areas in ML and NLP in 2021.
Machine Learning for Healthcare (Spring 2019)
By Massachusetts Institute of Technology (MIT)
π¬ 25 video lessons
β° 33 hours
π¨βπ« Prof. Peter Szolovits
π¨βπ« Prof. David Sontag
https://www.classcentral.com/course/mit-opencourseware-machine-learning-for-healthcare-spring-2019-40955/classroom
#ml #machinelearning #healthcare #MIT
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By Massachusetts Institute of Technology (MIT)
π¬ 25 video lessons
β° 33 hours
π¨βπ« Prof. Peter Szolovits
π¨βπ« Prof. David Sontag
https://www.classcentral.com/course/mit-opencourseware-machine-learning-for-healthcare-spring-2019-40955/classroom
#ml #machinelearning #healthcare #MIT
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Rules of Machine Learning:
Best Practices for ML Engineering
Author: Martin Zinkevich
This document is intended to help those with a basic knowledge of machine learning get thebenefit of best practices in machine learning from around Google.
π 43 ML Rules to follow
π http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
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Best Practices for ML Engineering
Author: Martin Zinkevich
This document is intended to help those with a basic knowledge of machine learning get thebenefit of best practices in machine learning from around Google.
π 43 ML Rules to follow
π http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
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Graph ML and deep learning courses
This is another post on your request. Other courses you requested will be shared in following days.
Geometric Deep learning course
AMMI21
π¨βπ« Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar VeliΔkoviΔ
π12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
π Course link: https://geometricdeeplearning.com/lectures/
Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan GΓΌnnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
π Course link: https://www.in.tum.de/daml/teaching/mlgs/
Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
π¬ 60 Videos
β° 30h
π Course link
Python For Data Science (Udemy)
This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY
Rating βοΈ: 4.1 out of 5
Students π¨βπ: 65,523 students
Duration β°: 3hr 55min of on-demand video
π Course link
Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating βοΈ: 4.6 out of 5
Students π¨βπ: 34,785
Duration β°: 1hr 59min of on-demand video
π Course link
There is also this cool blogpost by GordiΔ Aleksa:
How to get started with Graph Machine Learning
And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
π Book link
#graphML #ML #machinelearning #deeplearning #python
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πJoin @bigdataspecialist for moreπ
This is another post on your request. Other courses you requested will be shared in following days.
Geometric Deep learning course
AMMI21
π¨βπ« Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar VeliΔkoviΔ
π12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
π Course link: https://geometricdeeplearning.com/lectures/
Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan GΓΌnnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
π Course link: https://www.in.tum.de/daml/teaching/mlgs/
Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
π¬ 60 Videos
β° 30h
π Course link
Python For Data Science (Udemy)
This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY
Rating βοΈ: 4.1 out of 5
Students π¨βπ: 65,523 students
Duration β°: 3hr 55min of on-demand video
π Course link
Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating βοΈ: 4.6 out of 5
Students π¨βπ: 34,785
Duration β°: 1hr 59min of on-demand video
π Course link
There is also this cool blogpost by GordiΔ Aleksa:
How to get started with Graph Machine Learning
And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
π Book link
#graphML #ML #machinelearning #deeplearning #python
ββββββββββββββ
πJoin @bigdataspecialist for moreπ
Geometricdeeplearning
GDL Course
Grids, Groups, Graphs, Geodesics, and Gauges
Deep Learning Do It Yourself!
This site collects resources to learn Deep Learning in the form of Modules available through the sidebar on the left.
https://dataflowr.github.io/website/
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This site collects resources to learn Deep Learning in the form of Modules available through the sidebar on the left.
https://dataflowr.github.io/website/
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Forwarded from Coding interview preparation
π Book link
#machinelearning #ml #datascience
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#machinelearning #ml #datascience
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Lectures for UC Berkeley CS 182: Deep Learning
Spring 2021
π¬ 66 videos
β° 26 hours
https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A
#deeplearning
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Spring 2021
π¬ 66 videos
β° 26 hours
https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A
#deeplearning
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YouTube
Deep Learning: CS 182 Spring 2021
Lectures for UC Berkeley CS 182: Deep Learning.
The Incredible PyTorch
A curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
#pytorch
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A curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
#pytorch
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FOUNDATIONS OF MACHINE LEARNING
by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
π¬ 30 video lessons with slides
β° 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
π¬ 30 video lessons with slides
β° 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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